class_docstr: Downsample with both maxpooling and avgpooling,
  double the channel size by concatenating the downsampled feature maps.
docstr: Args:
      x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...]).
  
  Returns:
      Tensor in shape (batch, 2*channel, spatial_1[, spatial_2, ...]).
docstr_tok: ["Args",":","x",":","Tensor","in","shape","(","batch",",","channel",",","spatial_1","[",",","spatial_2",",","...","]",")",".","Returns",":","Tensor","in","shape","(","batch",",","2","*","channel",",","spatial_1","[",",","spatial_2",",","...","]",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...]).
  
      Returns:
          Tensor in shape (batch, 2*channel, spatial_1[, spatial_2, ...]).
      """
      return torch.cat([self.max_pool(x), self.avg_pool(x)], dim=1)
  
file_path: MONAI/monai/networks/blocks/downsample.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.layers.factories import Pool
  from monai.utils import ensure_tuple_rep
label_desc: Use image node_id 2 for calling the MaxAvgPool obj's underlying member method code with example usage: obj.forward(x) and returns: torch